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A McKinsey survey showed that 80% of supply chain executives expect to or are already using AI/ML in planning. This is a move in the right direction, as demand forecasting is essential for resilient and efficient supply chain management. The right implementation enables supply chain leaders to accurately predict and identify changes in future customer AI Use Cases for Supply Chain Optimization demand. This, in turn, boosts revenue, given the improved pricing and reduced inventory stockout that follow effective demand forecasting. With customer expectations changing quickly and getting more diverse, businesses now rely on AI-powered supply chain tools to glean more demand-related insights — and tune their production strategies accordingly.

The consumer goods leader, P&G, has one of the most complex supply chains with a massive product portfolio. The company excellently leverages machine learning techniques such as advanced analytics and application of data for end-to-end product flow management. Last-mile delivery is a critical aspect of the entire supply chain as its efficacy can have a direct impact on multiple verticals, including customer experience and product quality.

Top 10 use cases for Machine Learning in Supply Chain

The relationship begins with blockchain, which migrates from a traditional business network and brings together data of good quality across partners. Next add AI, which takes blockchain data, derives a meaningful context from it and generates powerful insights concerning potential benefits. Meanwhile, IoT acts as the interface or sensor, working at the edges to convert the physical into the virtual. So, buying has endured a tectonic change as more product classifications resort to electronic channels rather than brick-and-mortar stores. The management actions taken in warehouses will support additional processes, such as manifests and trend analyses. In doing so, the business can examine various storage units before being compared to validation records from radiography images.

As a supply chain owner or C-level executive, you struggle to reduce inventory imbalances. At the same time, you want to achieve ultimate visibility and transparency across all departments. Unfortunately, the supply chain generates too much data, complicated to store and analyze. Bots enabled with computer vision and AI/ML can be used to automate repetitive tasks in inventory management, such as scanning inventory in real time. The global furniture brand Ikea has also developed a demand forecasting tool based on AI, which uses historical and new data to provide accurate demand forecasts.

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Since all AI systems are unique and different, this is something that supply chain partners will have to discuss in depth with their AI service providers. Only a third of companies ushering in AI-driven transformation performs a diagnostic audit before rolling out the technology. With an all-rounded assessment carried out, define the supply chain digitization strategy, and make sure it reflects the findings. It makes sense to start with digitizing one segment of the supply chain that shows the highest value-creation potential to drive ROI faster. And once the base solution is rolled out, you could evolve further, both vertically, expanding the list of available features, and horizontally, extending the capabilities of AI to other supply chain segments. Imagine that artificial intelligence has analyzed your data and accurately predicted how much to order at each time interval based on past sales trends.

Product localization and identification help you find products that are hard to access, or which have fallen on the floor, got lost, or wedged between other items—and so on. This can help warehouse managers prevent inventory shrinkage more effectively. Mosaic Data Science carries unique expertise around predicting demand and optimizing supply inventory across various business units.

Challenges

At Gramener, we have helped supply chain leaders improve efficiency by up to 30% using our customized AI & ML solutions. It can also become increasingly challenging to manage when companies grow in size and complexity. The Future of AI in the supply chain presents a scenario where automation will play a prominent role in how companies manage their supply chains. The use of AI is applicable throughout the different stages of the supply chain process. For example, IoT-enabled devices help track the status and health of shipments in transit. With Artificial Intelligence playing a significant role, customers can now expect more options, quicker adaptability to changing markets, and altered product mix by businesses.

Thus, in the future, there will be more companies that use AI in supply chains. AI-enabled computer vision systems are also changing how supply chains operate. From improving quality control to inventory management, computer vision has various applications in supply chain optimization. Developing accurate demand plans and the visibility to make early sourcing decisions are crucial to the intelligent supply chain.

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Also, by constantly learning over time, it continuously improves on these recommendations as relative conditions change. Lack of complete visibility into existing product portfolios due to unplanned events, plant shutdowns, or transportation problems makes this task even more convoluted. A typical smart supply chain framework includes multiple products, spare parts, and critical components, which are responsible for accurate outcomes. In many supply chain industries, these products or parts can be defined using multiple characteristics that take a range of values. Also, in many cases, products and parts are also phased-in and phased-out regularly, which can cause proliferation leading to uncertainties and the bullwhip-effects up and down the supply chain. Their offerings are helpful for processes across the supply chain, from procurement to payments.

For digitizing quality control, consider Fujitsu Advanced Image Recognition (F|AIR). This platform is a perfect solution for visual product inspection and defect detection. Thanks to this intelligent algorithm, the platform is more precise in object detection than other machine vision software. Sustainability is a growing concern of supply chain managers since most of an organization’s indirect emissions are produced through its supply chain. AI can help improve supply chain operations to make them greener and more sustainable.

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Using Project Bonsai, one can build a brain to dynamically optimize regional inventory replenishment to create more stable and predictable stocking levels, thereby avoiding lost sales. Your ML developers need to determine the right data sources for your AI project. They also need to decide the data types to ensure the supply chain has enough information. Next, you and your team will start creating a project scope for proof of concept. In this way, your development team will concentrate on the most critical aspects of your supply chain. As a result, you will receive the right type of AI that drives meaningful outcomes and uncover a clear path for further improvements.

• The company excellently leverages machine learning techniques such as advanced analytics and application of data for end-to-end product flow management.
• Feeding off historical operational data, AI could help identify and correct operational inefficiencies in real time, providing an in-depth look into the supply chain performance, opportunities, and risks.
• The business case has been made, and it is up to individual companies to continue adapting artificial intelligence to their advantage.
• AI can automate many repetitive tasks and deliver significant return on investment, but it cannot replace people entirely.
• The increase or decrease in the price is governed by on-demand trends, product lifecycles, and stacking-the-product against the competition.
• Maltaverne says they can be used to design supply chains, analyze scenarios, build knowledge and optimize operations.

Artificial Intelligence and Machine Learning have fast-tracked the digital transformation of logistics and supply chains globally. There is a significant disruption in logistics, shortages in materials, skills, and labor, not to mention a rise in prices due to the COVID-19 pandemic and the war in Ukraine. This has forced companies to reimagine and reinvent the use cases for the supply chain with AI and ML.

This enables supply chain companies to have much better insights and help them achieve accurate forecasts. A report by McKinsey also indicates that AI and ML-based implementations in supply chain can reduce forecast errors up to 50%. There are several benefits of accurate demand forecasting in supply chain management, such as decreased holding costs and optimal inventory levels. Using intelligent machine learning software, supply chain managers can optimise inventory and find most suited suppliers to keep their business running efficiently. Understanding the root cause of stockouts and predicting accurate demand trends with better lead times from suppliers to reduce stock-outs. AI driven models help in programming autonomous vehicles and robots that are commonly used in warehouses.

Studies suggest that AI and Machine Learning can deliver unprecedented value to supply chain and logistics operations. Over the past years, artificial intelligence has become a vital element of a resilient supply chain. AI-based supply chain management tools are helping organizations speed up the flow of materials and finished products, cut down operational costs, and effectively navigate through changes. Symbotic builds and designs AI-powered robots to help businesses automate their workflows. Their solutions further enable enterprises to support their supply chain with advanced solutions.

Such robots will identify patterns, predict out-of-stock items, orders, and even returns. Watch how AI can utilize data generated from customers to create accurate demand forecasts and adjust them in real time to make the supply chain smarter and more robust. And while investing in the initial AI technologies for the supply chain will cost you money, the long-term benefits far outweigh any short-term losses. Inventory optimization is a complicated component of supply chain management that is vulnerable to many internal and external factors. It involves having the right inventory to meet your demand, and buffer against unexpected disruption, while avoiding wasteful surplus.

Symbotic’s offerings are primarily towards warehouse automation to reduce costs and improve efficiencies. To use Artificial Intelligence in logistics and supply chain management, consider integrating automated robots. Such robots will streamline product picking, unloading pallets, and even packing items. Apart from saving operating costs, robots can provide you with data-based decision making. Whether launching a new product or restocking inventory, it is important to maintain a predictive strategy rather than being reactionary. This is where ML and AI can help by analyzing previous data to pinpoint future demand and fortify pricing decisions.